The Effect of Forestry Roads on Access to Remote Fishing Lakes in Northern Ontario, Canada
Bibliographic record
Abstract
Abstract Lakes without road or vehicular trail access (i.e., remote lakes) are becoming increasingly scarce in North America. In the Boreal Shield, road construction for forestry operations is probably the prime factor affecting the scarcity of remote lakes. To assess the effects of forestry roads on lake access, this paper develops and tests a model that predicts the occurrence of road or vehicular trail access to lakes in northern Ontario. The results of a probit model support the hypothesis that increased forestry activity near lakes (as measured by road density within 1 km of the lake and the proximity of the lake to two-lane roads) results in increased likelihood of access. We also hypothesized that lake size, the presence of particular fish species, and the proximity of the lake to human communities would increase the likelihood of access. Except for no effect from the presence of trout Salvelinus species, the analyses supported these hypotheses. The predictive validity of the model was tested with holdout data from two other areas of northern Ontario. Strong support for the model was found from analyses of receiver operating characteristic curves for the two holdout data sets. Management scenarios from the model were used to illustrate the potential effects of forestry roads on access development to remote lakes. Our model predicts that in areas where forestry operations occur, development of access to lake shorelines will probably occur, especially on large-sized lakes containing walleyes Sander vitreus. Proactive management involving the closure of forestry roads is probably needed to retain remote lakes in areas with high levels of forestry activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".